Introduction

This document is meant to provide rationale for performing data analysis and statistical analysis for all data presented in Characterizing the Physiology of Circulatory Arrest in Humans. The code presented here is meant to be a single file that with the 3 data file dependencies (main_data.xlsx, time_series_signals.csv, timing.csv), which will provide all logic behind how figures were created and how all statistics were run. As this code was developed by JDB from 2023 onwards, some library dependencies in R may alter the output of the data. As such, all library versions are herein reported to be able to hopefully provide readers an easy avenue to replicate the supported data as shared on figshare. Please let Dr. Mypinder Sekhon or myself know if you have any questions or concerns.

Dependencies

R Library Version Number
\(R\) v4.3.1
\(tidyverse\) v2.0.0
\(rstatix\) v0.7.2
\(lme4\) v1.1-34
\(ggplot2\) v3.5.1
\(ggrepel\) v0.9.6
\(ggpubr\) v0.6.0
\(markdown\) v1.13
\(readxl\) v1.4.3
\(kableExtra\) v1.4.0
\(dplyr\) v1.1.3

1 Demographics

Sex of the Critically Ill Patient Cohort (N = 32) & Medical Assistance in Dying Cohort (N=3)
Sex Count Prevalence
Male 28 80%
Female 7 20%
Diagnosis Prevalence in the Critically Ill Patient Cohort (N = 32)
Diagnosis Count Prevalence
HIBI 15 46.88%
Sepsis 6 18.75%
TBI 6 18.75%
ICH 4 12.5%
SAH 1 3.12%
Continuous Variables for Critically Ill Patient Cohort
variable n min max median q1 q3 iqr mad mean sd se ci
Age 30 32.000 87.000 63.500 50.000 68.000 18.000 10.378 59.300 13.593 2.482 5.076
SaO2 30 88.000 97.000 96.000 94.000 96.000 2.000 1.483 95.067 2.016 0.368 0.753
SjvO2 26 54.000 91.000 80.500 76.250 86.750 10.500 8.896 77.923 11.457 2.247 4.628
SO2_av 26 5.000 42.000 14.500 8.500 20.750 12.250 9.637 17.115 11.262 2.209 4.549
PaO2 30 70.000 232.000 100.500 89.500 111.750 22.250 17.050 104.400 29.073 5.308 10.856
PjvO2 20 32.000 71.000 46.000 40.000 56.750 16.750 14.085 48.200 11.723 2.621 5.487
PO2_av 20 25.000 187.000 49.000 36.000 71.250 35.250 24.463 58.850 36.555 8.174 17.108
PaCO2 30 26.000 47.000 36.500 33.250 38.750 5.500 4.448 36.633 4.867 0.889 1.817
PjvCO2 20 33.000 52.000 41.500 37.750 44.500 6.750 5.930 41.650 5.631 1.259 2.636
PCO2_av 20 -13.000 0.000 -3.500 -7.000 -2.000 5.000 3.706 -4.350 3.438 0.769 1.609
HCO3_a 30 9.968 30.847 23.242 21.875 25.675 3.799 3.027 22.493 4.868 0.889 1.818
HCO3_jv 20 16.430 30.418 24.820 22.156 26.919 4.763 3.351 24.374 3.803 0.850 1.780
HCO3_av 20 -4.514 1.950 -0.521 -1.723 0.453 2.176 1.485 -0.688 1.620 0.362 0.758
pH_a 30 7.090 7.490 7.425 7.392 7.450 0.058 0.037 7.402 0.091 0.017 0.034
pH_jv 20 7.320 7.460 7.390 7.350 7.420 0.070 0.044 7.388 0.039 0.009 0.018
pH_av 20 -0.050 0.080 0.030 0.020 0.053 0.032 0.015 0.036 0.028 0.006 0.013
Hb 30 65.000 139.000 91.000 81.750 101.500 19.750 14.826 94.133 17.502 3.195 6.535
Lactate 30 0.500 27.000 1.000 0.800 1.550 0.750 0.445 3.003 5.666 1.034 2.116
Creatinine 30 34.000 492.000 84.500 57.500 151.000 93.500 57.821 123.267 105.765 19.310 39.493
Troponin 24 4.000 9598.000 89.000 27.500 424.000 396.500 117.867 1081.125 2543.997 519.291 1074.236
Bilirubin 29 5.000 146.000 8.000 6.000 15.000 9.000 4.448 23.069 35.901 6.667 13.656
Albumin 28 12.000 33.000 23.500 21.000 26.250 5.250 3.706 23.071 4.578 0.865 1.775
Oxygenation Index 30 2.236 23.790 7.389 5.072 8.588 3.517 3.396 8.242 4.904 0.895 1.831
PETCO2 28 17.250 39.750 30.848 27.281 32.738 5.456 3.947 29.451 4.888 0.924 1.895
DS_fraction 28 1.923 53.833 14.669 9.634 30.855 21.221 14.368 20.359 13.869 2.621 5.378
PF_ratio 30 124.000 532.000 287.143 210.714 327.857 117.143 88.956 287.296 99.419 18.151 37.124
Vasopressors 30 0.000 120.000 0.000 0.000 5.000 5.000 0.000 7.667 22.640 4.134 8.454
Midazolam 30 0.000 15.000 3.500 0.000 5.750 5.750 5.189 4.100 4.029 0.736 1.504
Hydromorphone 30 0.000 2.000 0.500 0.000 1.000 1.000 0.741 0.705 0.719 0.131 0.268
Midazolam_bolus 30 0.000 10.000 0.000 0.000 0.000 0.000 0.000 1.067 2.420 0.442 0.904
Hydromorphone_bolus 30 0.000 6.000 0.000 0.000 2.750 2.750 0.000 1.200 1.937 0.354 0.723
MCA 29 28.270 110.100 46.560 33.270 60.590 27.320 19.733 51.363 20.914 3.884 7.955
PCA 29 12.840 84.580 32.080 27.540 40.210 12.670 9.296 36.455 15.606 2.898 5.936
SvO2 15 32.480 78.900 66.880 62.130 72.505 10.375 8.777 65.314 11.795 3.046 6.532
CO 15 2.763 9.450 6.390 5.169 7.655 2.486 1.868 6.371 1.973 0.509 1.093
CVP 27 1.880 21.670 10.020 7.535 13.565 6.030 4.566 10.325 5.251 1.011 2.077
MAP 30 55.140 118.900 74.750 64.690 92.197 27.507 17.732 79.841 18.370 3.354 6.860
ICP 29 4.000 37.000 15.000 11.000 19.000 8.000 5.930 15.930 7.764 1.442 2.953
CPP 29 27.590 98.290 61.680 52.950 76.600 23.650 17.228 64.763 17.249 3.203 6.561
MCA_FVD 29 13.950 79.570 29.090 20.350 35.970 15.620 12.958 32.155 16.183 3.005 6.156
HR 30 56.000 126.000 86.000 78.750 95.500 16.750 13.343 87.700 15.892 2.902 5.934
ECHO_LVEF 16 20.000 65.000 57.000 40.250 65.000 24.750 11.861 50.500 15.122 3.780 8.058
FIO2 30 25.000 75.000 35.000 30.000 40.000 10.000 7.413 38.000 10.875 1.986 4.061
PEEP 30 5.000 12.000 8.000 5.000 10.000 5.000 2.965 7.767 2.445 0.446 0.913
PSV 30 5.000 14.000 8.000 5.000 10.000 5.000 3.706 8.267 2.852 0.521 1.065
Mv 30 6.840 11.040 9.000 8.155 9.735 1.580 1.186 8.977 1.101 0.201 0.411
Tv 30 380.000 610.000 460.000 421.250 497.500 76.250 59.304 467.833 54.562 9.962 20.374
RR 30 15.000 24.000 19.500 17.250 21.000 3.750 2.965 19.333 2.631 0.480 0.982
a HCO3- values were calculated from the Henderson-Hasselbach equation using PCO2 and pH values.
b Physiologic Dead Space was calculated as per Bohr’s Method from West’s Respiratory Physiology 11th Edition.
c av is for arterial-to-jugular venous gradients denoting changes that occur across the brain.

2 Main Text Data

FIGURES

Figure 1

Do the MCA and PCAv have a difference in timing from when they give out?

The time from WLST to cessation of PCAv was less than the time from WLST to cessation of MCAv (N = 28, r = 0.51, P = 0.01) with a large effect size.

Figure 2

Figure 3

Selection of Linear Mixed Effects Models after Comparison for rSO2.
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
MODEL1 4 21753.04 21777.26 -10872.52 21745.04 NA NA NA
MODEL2 4 23028.27 23052.49 -11510.13 23020.27 0.000 0 NA
MODEL3 6 21398.50 21434.84 -10693.25 21386.50 1633.766 2 0
a Model 1 - Random Intercept
b Model 2 - Random Slope
c Model 3 - Random Slope and Random Intercept (Correlated)

Data Processing Notes:
For linear mixed-effects models, we can choose either random intercepts (Model 1), random slopes (Model 2), or random slopes and random intercepts (Model 3). Visually, by just plotting the data we see that there is heterogeneity of slopes which is not surprising from a physiologic perspective so Model 2 and Model 3 seem more likely that they would fit. We also don’t necessarily think that the intercepts will be identical between patients as perhaps cerebral blood velocities cease while there is still some presence of MAP (which we did eventually find). As such, Model 3 was the intuitively most accurate from a physiologic perspective when we first started considering this data.

Selection of Linear Mixed Effects Models after Comparison for MCAv.
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
MODEL1 4 32246.45 32271.80 -16119.23 32238.45 NA NA NA
MODEL2 4 31337.90 31363.25 -15664.95 31329.90 908.5477 0 NA
MODEL3 6 31191.75 31229.78 -15589.88 31179.75 150.1523 2 0
a Model 1 - Random Intercept
b Model 2 - Random Slope
c Model 3 - Random Slope and Random Intercept (Correlated)

Selection of Linear Mixed Effects Models after Comparison for PCAv.
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
MODEL1 4 25187.86 25212.42 -12589.93 25179.86 NA NA NA
MODEL2 4 24949.65 24974.21 -12470.82 24941.65 238.2079 0 NA
MODEL3 6 24687.37 24724.21 -12337.68 24675.37 266.2814 2 0
a Model 1 - Random Intercept
b Model 2 - Random Slope
c Model 3 - Random Slope and Random Intercept (Correlated)

As MCAv and PCAv have different starting baselines, is there an interaction effect when baselines are normalized to 100% of CBv going down to 0% CBv?

Linear Mixed Models for whether there is an interaction between the MCAv and PCAv for MAP responses.
term npar sumsq meansq statistic
ABP 1 0.0080909 0.0080909 67.066312
Vessel 1 0.0001516 0.0001516 1.256289
ABP:Vessel 1 0.0001355 0.0001355 1.123480

Figure 4

Figure 5

Data Processing Notes:
Tau and UCH-L1 were not linear over the full dilution range (4x, 20x, 100x). We had internal discussion about whether we only include Tau and UCH-L1 data at 4x, but we decided that as paired samples were run at the same dilution and our main question whether their are changes from Pre-WLST to Post-WLST, we decided to keep all dilution levels. There is pseudocode embedded in this RMarkdown to re-run the analysis without the higher dilutions for Tau and UCH-L1 (see Serum Proteomics code chunk). The positives of keeping the extra dilution data is the higher sample N for comparison for paired analysis.

Serum blood-based neurologic biomarker comparisons using the Quanterix Argo HT platform. Red is for arterial, blue is for jugular venous, and purple is for cerebral A-V gradients.

Serum blood-based neurologic biomarker comparisons using the Quanterix Argo HT platform. Red is for arterial, blue is for jugular venous, and purple is for cerebral A-V gradients.


**Figure 5S.** Arterial plasma proteomic comparisons between healthy control participants and critically ill patients prior to withdrawal of life-sustaining treatment.

Figure 5S. Arterial plasma proteomic comparisons between healthy control participants and critically ill patients prior to withdrawal of life-sustaining treatment.

**Figure 5T.** Arterial plasma proteomic comparisons between critically ill patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure dropped below 60 mmHg.

Figure 5T. Arterial plasma proteomic comparisons between critically ill patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure dropped below 60 mmHg.

**Figure 5T.** Arterial plasma proteomic comparisons between critically ill patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure dropped below 60 mmHg.

Figure 5T. Arterial plasma proteomic comparisons between critically ill patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure dropped below 60 mmHg.

Figure 6

**Figure 6.** Physiologic responses to the last agonal breath during the dying process in critically ill humans

Figure 6. Physiologic responses to the last agonal breath during the dying process in critically ill humans

TABLES

Timing Data

Time from WLST to Cessation of Cerebral Blood Velocities and Pulseless Electrical Activity
ID WLST to MCA WLST to PCA WLST to PEA MCA to PEA (s) PCA to PEA (s)
Sepsis 1 380 380 389 9 9
HIBI 2 2105 2101 2106 1 5
HIBI 3 2599 2520 2644 45 124
HIBI 4 2648 2628 2750 102 122
HIBI 5 1160 1250 1312 152 62
TBI 6 11582 11582 11640 58 58
HIBI 7 415 415 450 35 35
HIBI 8 12329 12339 12481 152 142
Sepsis 9 4376 4373 4883 507 510
HIBI 10 11847 11791 11844 -3 53
HIBI 11 1852 1530 2179 327 649
HIBI 12 521 521 533 12 12
ICH 13 NA NA NA NA NA
Sepsis 14 11450 11374 11515 65 141
TBI 15 7817 7906 9945 2128 2039
HIBI 16 22185 21735 22500 315 765
ICH 17 3835 540 4085 250 3545
TBI 18 7380 7380 7625 245 245
Sepsis 19 467 NA 738 271 NA
ICH 20 13990 13990 14030 40 40
HIBI 21 2390 2165 2396 6 231
Sepsis 22 228 228 238 10 10
SAH 23 710 230 769 59 539
HIBI 24 6587 6463 6829 242 366
HIBI 25 NA NA NA NA NA
ICH 26 4570 3550 4620 50 1070
TBI 27 2164 2164 2300 136 136
TBI 28 1685 1575 1820 135 245
HIBI 29 6185 5992 7815 1630 1823
TBI 30 NA NA NA NA NA
Sepsis 31 1905 2424 2839 934 415
HIBI 32 1572 825 1748 176 923
MAID 1 969 769 1024 55 255
MAID 2 250 250 277 27 27
MAID 3 167 167 174 7 7
Group Summary Data - Seconds
variable n min max median q1 q3 iqr mad mean sd se ci
WLST to MCA (s) 13 415 22185 2599 1852 6587 4735 3080.843 5601.769 6372.885 1767.520 3851.096
WLST to PCA (s) 13 415 21735 2520 1530 6463 4933 2963.717 5496.154 6293.968 1745.633 3803.407
WLST to PEA (s) 13 450 22500 2644 2106 7815 5709 3129.769 5833.769 6450.279 1788.986 3897.865
WLST to EA (s) 13 536 23220 2753 2231 8146 5915 2074.157 6115.846 6492.654 1800.738 3923.472
WLST to SBP 60 (s) 13 205 22125 2370 1789 6580 4791 2980.026 5355.769 6277.169 1740.973 3793.255
MCA to PEA (s) 13 -3 1630 102 12 242 230 142.330 232.000 436.077 120.946 263.519
PCA to PEA (s) 13 5 1823 124 53 366 313 158.638 337.615 507.752 140.825 306.832
MCA to EA (s) 13 6 1961 194 126 708 582 265.385 514.077 614.175 170.341 371.142
PCA to EA (s) 13 15 2154 233 142 1030 888 293.555 619.692 686.150 190.304 414.636
a The average time it took for WLST to PEA <5 mmHg was: 82 minutes.
b The average time it took for WLST to no MCA signal was: 77 minutes.
c The average time it took for WLST to no PCA signal was: 76 minutes.

Brain pathology

Data Processing Notes:
The pathology data is scored as ranked categories which presents some challenges when there are a limited number of brain autopsies to analyze. As such, for this initial manuscript, the brain autopsy data was binary classified as either the presence of pathology or absence of pathology. The chronic neuropathology is classified as None, Mild, Moderate, and Severe while the acute inflammation neuropathology is classified as None, Very Mild, Mild, Moderate, and Severe. An alternative approach to analysis in the future would be using Kruskal-Wallis comparisons with a Dunn’s test post hoc to determine if there are differences between different levels of pathology present.

acute_inflammation n
Mild 12
Moderate 6
None 6
Severe 2
Very Mild 2
chronic_severity n
Mild 10
Moderate 8
None 8
Severe 2
Cohort variable n min max median q1 q3 iqr mad mean sd se ci
Critically Ill Patient Cohort weight 28 942 1811 1387.5 1286.25 1459.25 173 150.484 1388.643 180.818 34.171 70.114
[1] 28
acute n
No 6
Yes 22
chronic n
No 8
Yes 20
n estimate p conf.low conf.high method alternative p.signif
28 1.31925 1 0.0956354 12.44587 Fisher’s Exact test two.sided ns
n statistic p df method p.signif
35 0 1 1 Chi-square test ns
Relationships between the presence of brain neuropathology and various time intervals during the dying process.
Pathology Duration P-Value
Chronic Neuropathology SPO2_70_to_PEA 0.0618
Chronic Neuropathology SPO2_70_to_ASYS 0.0800
Chronic Neuropathology SBP50_to_PEA 0.0849
Chronic Neuropathology SPO2_80_to_ASYS 0.1150
Acute Inflammation Neuropathology SBP50_to_PEA 0.1360
Chronic Neuropathology MAP60_to_ASYS 0.1440
Chronic Neuropathology MAP60_to_PEA 0.1600
Chronic Neuropathology SBP60_to_ASYS 0.1650
Chronic Neuropathology SPO2_80_to_PEA 0.1730
Chronic Neuropathology SBP50_to_ASYS 0.1960
Chronic Neuropathology MCA_to_PEA 0.2110
Chronic Neuropathology MCA_to_EA 0.2380
Acute Inflammation Neuropathology SPO2_70_to_PEA 0.2420
Acute Inflammation Neuropathology SPO2_80_to_PEA 0.2730
Chronic Neuropathology MAP50_to_ASYS 0.3110
Chronic Neuropathology WLST_to_EA 0.3380
Chronic Neuropathology SBP60_to_PEA 0.3670
Chronic Neuropathology WLST_to_PEA 0.3670
Chronic Neuropathology WLST_to_PCA 0.3970
Chronic Neuropathology WLST_to_VE 0.4040
Acute Inflammation Neuropathology MCA_to_PEA 0.4470
Acute Inflammation Neuropathology PCA_to_PEA 0.4470
Chronic Neuropathology PCA_to_PEA 0.4530
Acute Inflammation Neuropathology SPO2_80_to_ASYS 0.4570
Chronic Neuropathology WLST_to_SBP50 0.4610
Acute Inflammation Neuropathology SPO2_70_to_ASYS 0.4940
Acute Inflammation Neuropathology WLST_to_SPO2_80 0.4940
Chronic Neuropathology WLST_to_MCA 0.4950
Chronic Neuropathology WLST_to_SPO2_80 0.4950
Chronic Neuropathology PCA_to_EA 0.5310
Acute Inflammation Neuropathology MCA_to_EA 0.5330
Acute Inflammation Neuropathology SBP50_to_ASYS 0.5330
Acute Inflammation Neuropathology SBP60_to_PEA 0.5330
Chronic Neuropathology MAP50_to_PEA 0.5670
Acute Inflammation Neuropathology MAP50_to_ASYS 0.5730
Acute Inflammation Neuropathology MAP50_to_PEA 0.6140
Acute Inflammation Neuropathology PCA_to_EA 0.6140
Acute Inflammation Neuropathology WLST_to_SPO2_70 0.6920
Acute Inflammation Neuropathology WLST_to_MAP50 0.7000
Acute Inflammation Neuropathology WLST_to_MAP60 0.7000
Acute Inflammation Neuropathology WLST_to_PCA_diastolic 0.7000
Chronic Neuropathology WLST_to_MAP50 0.7240
Chronic Neuropathology WLST_to_MCA_diastolic 0.7240
Acute Inflammation Neuropathology SBP60_to_ASYS 0.7380
Acute Inflammation Neuropathology WLST_to_MCA_diastolic 0.7440
Chronic Neuropathology WLST_to_MAP60 0.7650
Chronic Neuropathology WLST_to_SBP60 0.8070
Acute Inflammation Neuropathology MAP60_to_PEA 0.8360
Acute Inflammation Neuropathology WLST_to_SBP50 0.8360
Acute Inflammation Neuropathology WLST_to_SBP60 0.8360
Chronic Neuropathology WLST_to_SPO2_70 0.8680
Acute Inflammation Neuropathology WLST_to_VE 0.8790
Acute Inflammation Neuropathology WLST_to_EA 0.8820
Chronic Neuropathology WLST_to_PCA_diastolic 0.8920
Acute Inflammation Neuropathology WLST_to_MCA 0.9290
Acute Inflammation Neuropathology WLST_to_PCA 0.9290
Acute Inflammation Neuropathology WLST_to_PEA 0.9290
Acute Inflammation Neuropathology MAP60_to_ASYS 0.9760
Relationships between the presence of brain neuropathology and Blood-Based Neurologic Biomarkers.
Pathology Biomarker Vessel P-Value
Acute Inflammation Neuropathology Nf-L Jugular 0.0322
Acute Inflammation Neuropathology Nf-L Arterial 0.0480
Chronic Neuropathology Tau Jugular 0.1150
Chronic Neuropathology Tau Arterial 0.1290
Acute Inflammation Neuropathology GFAP Jugular 0.1300
Acute Inflammation Neuropathology GFAP Arterial 0.1720
Acute Inflammation Neuropathology UCH-L1 Arterial 0.6070
Acute Inflammation Neuropathology UCH-L1 Jugular 0.6070
Chronic Neuropathology UCH-L1 Arterial 0.6830
Chronic Neuropathology Nf-L Jugular 0.7650
Chronic Neuropathology UCH-L1 Jugular 0.8490
Acute Inflammation Neuropathology Tau Jugular 0.8640
Chronic Neuropathology GFAP Jugular 0.8920
Chronic Neuropathology Nf-L Arterial 0.9350
Chronic Neuropathology GFAP Arterial 0.9780
Acute Inflammation Neuropathology Tau Arterial 1.0000

Heart pathology

variable n min max median q1 q3 iqr mad mean sd se ci
weight 22 326 918 421.5 377 444 67 55.597 446.773 135.708 28.933 60.17
Relationships between time intervals during the dying process and presence of heart pathology
CONDITION Time Interval group1 group2 n1 n2 statistic p
Coronary Artery Stenosis WLST_to_EA No Yes 11 9 85 0.00566
Coronary Artery Stenosis WLST_to_PEA No Yes 11 9 85 0.00566
Coronary Artery Stenosis WLST_to_MCA No Yes 11 9 82 0.01250
Left Anterior Descending Artery Stenosis WLST_to_EA No Yes 13 7 76 0.01450
Left Anterior Descending Artery Stenosis WLST_to_PEA No Yes 13 7 76 0.01450
Left Anterior Descending Artery Stenosis WLST_to_MCA No Yes 13 7 75 0.01860
Right Coronary Artery Stenosis WLST_to_EA No Yes 12 8 78 0.02010
Right Coronary Artery Stenosis WLST_to_PEA No Yes 12 8 78 0.02010
Coronary Artery Stenosis WLST_to_PCA No Yes 11 8 71 0.02590
Left Anterior Descending Artery Stenosis WLST_to_PCA No Yes 13 6 63 0.03650
Right Coronary Artery Stenosis WLST_to_MCA No Yes 12 8 75 0.03870
Right Coronary Artery Stenosis WLST_to_PCA No Yes 12 7 64 0.06830
Circumflex Stenosis WLST_to_PCA No Yes 16 3 29 0.63400
Circumflex Stenosis WLST_to_PEA No Yes 17 3 30 0.68900
Myocardial Pathology WLST_to_MCA No Yes 11 9 44 0.71000
Circumflex Stenosis WLST_to_EA No Yes 17 3 29 0.76500
Circumflex Stenosis WLST_to_MCA No Yes 17 3 29 0.76500
Myocardial Pathology WLST_to_EA No Yes 11 9 45 0.76600
Left Ventricle Pathology WLST_to_PEA No Yes 12 8 52 0.79200
Ventricular Pathology WLST_to_PEA No Yes 12 8 52 0.79200
Myocardial Pathology WLST_to_PEA No Yes 11 9 46 0.82400
Left Ventricle Pathology WLST_to_MCA No Yes 12 8 51 0.85100
Ventricular Pathology WLST_to_MCA No Yes 12 8 51 0.85100
Right Atrium Pathology WLST_to_EA No Yes 18 2 16 0.85300
Right Atrium Pathology WLST_to_MCA No Yes 18 2 16 0.85300
Left Atrium Pathology WLST_to_EA No Yes 18 2 16 0.85300
Left Atrium Pathology WLST_to_MCA No Yes 18 2 16 0.85300
Right Ventricle Pathology WLST_to_EA No Yes 18 2 16 0.85300
Right Ventricle Pathology WLST_to_MCA No Yes 18 2 16 0.85300
Atrial Pathology WLST_to_EA No Yes 18 2 16 0.85300
Atrial Pathology WLST_to_MCA No Yes 18 2 16 0.85300
Left Ventricle Pathology WLST_to_EA No Yes 12 8 50 0.91000
Ventricular Pathology WLST_to_EA No Yes 12 8 50 0.91000
Right Atrium Pathology WLST_to_PEA No Yes 18 2 17 0.94700
Left Atrium Pathology WLST_to_PEA No Yes 18 2 17 0.94700
Right Ventricle Pathology WLST_to_PEA No Yes 18 2 17 0.94700
Atrial Pathology WLST_to_PEA No Yes 18 2 17 0.94700
Myocardial Pathology WLST_to_PCA No Yes 10 9 45 1.00000
Right Atrium Pathology WLST_to_PCA No Yes 17 2 17 1.00000
Left Atrium Pathology WLST_to_PCA No Yes 17 2 17 1.00000
Right Ventricle Pathology WLST_to_PCA No Yes 17 2 17 1.00000
Left Ventricle Pathology WLST_to_PCA No Yes 12 7 42 1.00000
Ventricular Pathology WLST_to_PCA No Yes 12 7 42 1.00000
Atrial Pathology WLST_to_PCA No Yes 17 2 17 1.00000

Baseline Data Prior to WLST

Baseline variables prior to withdrawal of life sustaining treatments
variable n min max median q1 q3 iqr mad mean sd se ci
Mean Arterial Pressure 29 40.090 114.082 81.925 66.394 89.837 23.443 20.983 80.296 18.318 3.401 6.968
Peripheral Oxygen Saturation 28 69.417 100.000 98.035 95.967 99.017 3.049 1.927 96.400 5.725 1.082 2.220
Middle Cerebral Artery Blood Velocity 26 24.294 112.744 50.826 32.724 63.269 30.545 23.837 52.570 22.913 4.494 9.255
Posterior Cerebral Artery Blood Velocity 25 13.457 84.115 39.074 29.405 44.557 15.152 12.482 40.164 15.325 3.065 6.326
Systolic Blood Pressure 22 91.165 175.753 113.937 100.679 128.855 28.176 21.942 118.530 24.022 5.122 10.651
Diastolic Blood Pressure 22 38.398 84.131 58.568 45.778 69.379 23.601 17.558 58.909 13.340 2.844 5.915
Right Regional Cerebral Oxygen Sat 18 27.333 89.141 62.730 46.873 66.471 19.598 15.561 58.901 16.717 3.940 8.313
Left Regional Cerebral Oxygen Sat 18 36.661 81.523 60.853 49.644 68.017 18.373 13.652 58.984 12.596 2.969 6.264
Jugular Venous Oxygen Saturation 27 48.600 92.872 82.164 78.170 86.990 8.820 6.899 79.814 10.261 1.975 4.059
Brain Oxygen Extraction Fraction 27 1.147 37.783 15.592 11.585 21.394 9.808 7.331 16.941 8.613 1.658 3.407
Avg Regional Cerebral Oxygen Sat 18 33.184 85.332 61.526 48.007 68.206 20.199 15.021 58.980 14.448 3.405 7.185
Central Venous Pressure 20 1.611 31.869 11.255 5.945 15.434 9.490 6.873 11.402 7.552 1.689 3.534
Central Mixed Venous Oxygen Saturation 16 30.087 78.932 69.142 63.584 72.459 8.875 8.030 67.135 11.159 2.790 5.946
Whole Body Oxygen Extraction Fraction 16 20.204 56.221 29.230 27.243 33.460 6.217 5.896 30.670 8.229 2.057 4.385

3 Extended Display Items

FIGURES

eFigure 1

**eFigure 1.** Feasibility of data collection in critically ill and medical assistance in dying patient cohorts.

eFigure 1. Feasibility of data collection in critically ill and medical assistance in dying patient cohorts.

eFigure 2

**eFigure 2.** Relationships between sedative administration during the dying process and teh resultant length of dying process.

eFigure 2. Relationships between sedative administration during the dying process and teh resultant length of dying process.

eFigure 3

**eFigure 3.** Normalized timing signals to demonstrate trajectories throughout the dying process.

eFigure 3. Normalized timing signals to demonstrate trajectories throughout the dying process.

eFigure 4

eFigure 5

eFigure 6

Individual AUCs (mmHg s) of hypotensive burden for MAP < 65
ID AUC_diff
TBI 15 250.10
HIBI 21 547.00
HIBI 10 1112.75
HIBI 32 3613.30
Sepsis 22 3616.35
ICH 17 3654.25
HIBI 2 4255.75
TBI 27 4469.55
Sepsis 1 5914.80
HIBI 12 6227.65
HIBI 24 6352.15
ICH 26 7106.00
HIBI 4 8336.40
HIBI 3 8643.85
TBI 28 8723.95
HIBI 7 10063.25
SAH 23 13385.45
Sepsis 19 17207.09
HIBI 11 21739.20
HIBI 16 50437.20
Sepsis 31 54123.65
Sepsis 14 80295.90
TBI 18 92738.10
ICH 20 124439.00
Sepsis 9 141358.56
HIBI 8 144524.95
HIBI 29 164452.50
TBI 6 169438.70
a The largely different values is based on both the time and severity of injury
Individual AUCs (mmHg s) of hypotensive burden for MAP < 50
ID AUC
HIBI 21 427.450
HIBI 10 1198.200
Sepsis 22 2577.150
ICH 17 3737.250
HIBI 32 3972.800
TBI 27 5202.950
HIBI 2 5468.200
HIBI 12 5671.050
ICH 26 6415.350
Sepsis 1 6450.650
HIBI 24 8088.200
TBI 28 8153.288
HIBI 3 8552.950
HIBI 7 9743.350
Sepsis 19 11120.210
HIBI 11 11437.650
HIBI 4 11917.550
SAH 23 21094.050
Sepsis 14 28416.100
HIBI 16 29571.350
Sepsis 31 33264.050
TBI 18 99179.500
Sepsis 9 159146.490
ICH 20 161643.500
HIBI 8 169513.000
HIBI 29 220953.450
TBI 6 286902.550
a The largely different values is based on both the time and severity of injury
**eFigure 6E-L.** The relationship between area under the curve for hypotensive or hypoxemic burden and change in cerebral gradient biomarkers of the neurovascular unit.

eFigure 6E-L. The relationship between area under the curve for hypotensive or hypoxemic burden and change in cerebral gradient biomarkers of the neurovascular unit.

eFigure 7

Data Processing Notes:
The alamar NULISA data is a relative concentration which is normally expressed as log2 data. In order to perform data manipulations such as cerebral gradient analysis (AV; Arterial - Jugular venous concentrations), the data needs to be exponentiated, subtracted, then log transformed back into log2 data. You’ll note that individual AV data looks like nothing is close to zero and somewhat polarized on the x-axis. This is normal as the log2 changes, detectable differences, and some level of measurement error will make it very hard to get values basically right on zero so there will be some stochastic noise there.

The other issue with AV analysis is that it is not easily expressed as a log2 difference for volcano plots which is why the AV data is only presented in eFigure 7 as an exploratory analysis. Regardless, it is a way to determine what might being taken up by the brain or removed from the brain compared to control data which we feel can be an interesting future avenue for exploration.

**eFigure 7A.** Individual arterial plasma proteomic relationships between healthy control participants and patients prior to withdrawal of life-sustaining treatment.

eFigure 7A. Individual arterial plasma proteomic relationships between healthy control participants and patients prior to withdrawal of life-sustaining treatment.

**eFigure 7B.** Individual cerebral arterial-to-jugular venous gradient plasma proteomic relationships between healthy control participants and patients prior to withdrawal of life-sustaining treatment.

eFigure 7B. Individual cerebral arterial-to-jugular venous gradient plasma proteomic relationships between healthy control participants and patients prior to withdrawal of life-sustaining treatment.

These cerebral arterial-to-jugular venous gradients are an instantaneous release of biomarkers and will not represent bulk fluxes over time. Consequently, while statistics are one part of the story, they do not give a rate of release for biomarkers over time. To get at rate of release of biomarkers, more timepoints are needed in a short time frame to see how biomarkers are changing over a period of time/period of physiologic stress.

**eFigure 7C.** Individual arterial plasma proteomic relationships between patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure of <60 mmHg.

eFigure 7C. Individual arterial plasma proteomic relationships between patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure of <60 mmHg.

TABLES


4 Extra Individual Data Plots

Individual Data Plots

CBv vs MAP

Appendix & Resources

Abbreviation Library

Metrics Full Name
\(ABP\) Arterial blood pressure
\(CBF\) Cerebral blood flow
\(CPP\) Cerebral perfusion pressure
\(CVC\) Cerebrovascular conductance
\(DBP\) Diastolic blood pressure
\(HIBI\) Hypoxic ischemic brain injury
\(HR\) Heart rate
\(ICP\) Intracranial pressure
\(MAP\) Mean arterial pressure
\(MCAv\) Medial cerebral artery velocity
\(O_2EF\) Oxygen extraction fraction
\(P_{ET}CO_2\) Partial pressure of end-tidal carbon dioxide
\(P_aCO_2\) Arterial partial pressure of carbon dioxide
\(P_{bt}O_2\) Intraparenchymal pressure of oxygen
\(PCAv\) Posterior cerebral artery velocity
\(rSO_2\) Regional cerebral oxygen saturation
\(S_{jv}O_2\) Jugular venous oxygen saturation
\(S_pO_2\) Pulse oxygen saturation
Biomarkers Full Name
\(GFAP\) Glial fibrillary acidic protein
\(UCH-L1\) Ubiquitin carboxy-terminal hydorlase L1
\(Nf-L\) Neurofilament-light